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Record W2020735623 · doi:10.1088/0029-5515/47/9/032

Current profile control and optimization under dominant electron heating in HL-2A

2007· article· en· W2020735623 on OpenAlexfundno aff
Q. D. Gao, R. Budny, Yiming Jiao, K. Indireshkumar

Bibliographic record

VenueNuclear Fusion · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersInstitute of Circulatory and Respiratory HealthNational Natural Science Foundation of China
KeywordsLower hybrid oscillationPlasmaMaterials scienceAtomic physicsElectronBootstrap currentCyclotronCurrent (fluid)PhysicsToroidThermodynamicsNuclear physics

Abstract

fetched live from OpenAlex

The establishment of the current profile as in the hybrid scenario is studied under the condition of dominant electron heating in HL-2A. The scenarios with injecting lower hybrid (LH) and electron cyclotron (EC) waves are under numerical study. Carefully adjusting the position of non-inductive current driven by two groups of gyrotron, an optimized q -profile was obtained with q a = 3.78 and a weak shear region extending to ρ ∼ 0.45 (where ρ is the square-root of toroidal flux normalized to its value at the plasma boundary) in low-density discharges of . When 0.5 MW LH power in the current drive mode and 0.95 MW EC power mainly for plasma heating are used to control the current profile, a hybrid discharge scenario with a weak magnetic shear region extended to ρ = 0.6 and q a = 3.21 is established by controlling the EC absorption position. The mechanism of the LH wave absorption in the HL-2A plasma causes interplay of the distribution of the LH driven current with the modification of the plasma configuration, which constitutes non-linearity in the LH wave deposition. Due to the non-linearity the LH wave deposition position changes spontaneously or oscillates. The oscillatory behaviour caused by the non-linear effect of the LH wave deposition is analysed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.265
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2007
Admission routes1
Has abstractyes

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